DevOps & Cloud Engineering AI Enabled

Role & responsibilities

Job Description

Consultant: ATS DevOps & Cloud Engineering – AI Enabled

Experience - 6–8 years

Location-  Pune

Mode- Freelancer

Role Overview

We are looking for a hands-on DevOps / Cloud Engineer responsible for automating software delivery, managing cloud infrastructure, improving application reliability, and implementing secure, scalable cloud-native environments across AWS, Azure and GCP.

The candidate should have strong hands-on expertise in CI/CD, Infrastructure as Code, containers, Kubernetes, cloud services, monitoring and DevSecOps, along with practical experience using AI/GenAI tools to improve DevOps automation, troubleshooting and engineering productivity.

Required Skills

Must Have

• 6–8 years of hands-on DevOps / Cloud experience
• Strong experience in one major cloud – AWS / Azure / GCP
• Working knowledge of at least one additional cloud
• Strong CI/CD experience
• Terraform / Infrastructure as Code
• Docker
• Kubernetes
• Git / Git-based workflows
• Linux
• Scripting – Python / Bash / PowerShell
• Monitoring, logging and troubleshooting
• Strong understanding of networking fundamentals
• Security fundamentals / DevSecOps

AI – Must Have

• Practical experience using AI coding/productivity tools
• Understanding of AI-assisted DevOps automation
• Ability to use AI for troubleshooting, scripting and documentation while validating outputs

Good to Have

• AWS + Azure + GCP exposure
• Helm
• Argo CD / GitOps
• Prometheus / Grafana / OpenTelemetry
• Datadog
• Backstage / IDP
• Ansible
• Vault / secrets management
• FinOps / cloud cost optimization
• AI agents / Agentic AI
• MCP
• MLOps / AI workload deployment

Key Responsibilities

DevOps & CI/CD

• Design, implement and maintain CI/CD pipelines using tools such as GitHub Actions, Azure DevOps, Jenkins or GitLab CI.
• Automate build, test, deployment and release processes.
• Implement branching, release and deployment strategies including blue-green, canary and rolling deployments.
• Improve deployment reliability, speed and repeatability.

Multi-Cloud Engineering

• Work hands-on with AWS, Azure and/or GCP cloud environments.
• Provision and manage cloud infrastructure including compute, networking, storage, IAM and managed services.
• Understand equivalent services across cloud platforms and recommend appropriate solutions based on workload requirements.
• Support cloud migration, modernization and cost optimization initiatives.

Important: one cloud experience is mandatory + working exposure to other cloud rather than deep expertise in all three.

Infrastructure as Code & Automation

• Develop reusable infrastructure using Terraform or equivalent IaC tools.
• Automate infrastructure provisioning, configuration and environment management.
• Implement infrastructure standards, reusable modules and automated policy controls.
• Maintain version-controlled infrastructure and configuration.

Containers & Kubernetes

• Build and manage Docker containers and containerized applications.
• Work with Kubernetes / EKS / AKS / GKE environments.
• Support deployments, scaling, configuration, secrets and troubleshooting.
• Exposure to Helm, GitOps and tools such as Argo CD is preferred.

DevSecOps

• Integrate security into CI/CD and infrastructure workflows.
• Implement:

oSAST

oDAST

odependency scanning

ocontainer/image scanning

osecrets detection

• Follow secure cloud/IAM practices and support vulnerability remediation.

Observability & SRE

• Implement application and infrastructure monitoring, logging and alerting.
• Work with tools such as CloudWatch, Azure Monitor, GCP Operations Suite, Prometheus, Grafana, Datadog or OpenTelemetry.
• Support incident investigation, root-cause analysis and performance troubleshooting.
• Contribute to reliability, availability and operational excellence.

AI / GenAI Expectations

AI-Assisted DevOps

• Use AI tools such as GitHub Copilot, Amazon Q, Gemini, Claude or ChatGPT to improve:

oInfrastructure scripting

oYAML/pipeline development

oTerraform generation/review

otroubleshooting

olog analysis

odocumentation

otest automation

• Demonstrate ability to validate and secure AI-generated code/configuration rather than blindly accepting it.

AI is increasingly being incorporated into DevOps workflows for planning, coding, code review, security and operational troubleshooting.

AI / Agentic DevOps – Good to Have

• Exposure to AI agents / Agentic AI for IT operations.
• Understanding of how AI can support:

oincident triage

oroot-cause analysis

oautomated remediation

odeployment analysis

ocloud resource optimization

oinfrastructure monitoring

• Exposure to MCP (Model Context Protocol) or AI-agent integration with DevOps tools is a plus.

AWS, for example, now provides AI-powered DevOps capabilities that can query infrastructure, metrics, alarms, deployments and incident patterns using natural language.

Platform Engineering – Preferred

For stronger ATS-level candidates:

• Understanding of Platform Engineering / Internal Developer Platforms (IDP).
• Build reusable "Golden Paths" for development and deployment.
• Automate environment provisioning and developer self-service.
• Exposure to Backstage or similar developer portals is a plus.

Platform engineering is increasingly being positioned as the layer that provides reusable, self-service infrastructure, CI/CD and deployment capabilities to development teams.